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International Journal of Scientific and Research Publications, Volume 3, Issue 2, February 2013 705 ISSN 2250-3153 VI. CONCLUSION AND FUTURE WORK In this paper we have briefly described the working of the Kalman Filter eye tracking, mean shift eye tracking, one of the proposed driver fatigue detection system “Kalman filtering based fatigue detection system”. But, these systems and techniques have their respective disadvantage. Our proposed system can overcome the drawbacks and disadvantages of these systems. Further research will be focused on implementing the proposed idea in order to get the good results in detecting the driver fatigue. REFERENCES [1] Zutao ZHANG, Jiashu ZHANG “A new real- time ey tracking based on nonlinear unscented Kalman filter for monitoring driver fatigue ” at J Control Theory Appl, 2010 [2] W. Dong, X. Wu “Driver fatigue detection based on the distance of eyelid” at Proceeding of IEEE International Workshop on VLSI Design and Video Technology. New York: IEEE, 2005: 365 – 368. [3] Q. Ji, Z. Zhu, P. Lan. “Real time nonintrusive monitoring and prediction of driver fatigue” at IEEE Transactions on Vehicular Technology, 2004, 53(4): 1052 – 1068. [4] W. Horng, C. Chen, Y. Chang, et al. “Driver fatigue detection based on eye tracking and dynamic template matching” at Proceeding of the 2004 IEEE International Conference on Networking, Sensing & Control. New York: IEEE, 2004: 7 – 12. [5] S. J. Julier, J. K. Uhlmann “Unscented filtering and nonlinear estimation” at Proceedings of the IEEE, 2004, 92(3): 401 – 422. [6] H. Gu, Q. Ji, Z. Zhu “Active facial tracking for fatigue detection” at Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision. Piscataway: IEEE, 2002: 137 – 142. [7] D. Comaniciu, V. Ramesh “Mean shift and optimal prediction for efficient object tracking” at Proceedings of the International Conference of Image Processing. New York: IEEE, 2000: 70 – 73. [8] L. Hartley, T. Horberry, N. Mabbott, et al “Review of fatigue detection and prediction technologies” at National Road Transport Commission Report Melbourne: Murdoch University, 2000. [9] S. Singh, N. P. Papanikolopoulos “Monitoring driver fatigue using facial analysis techniques” at Proceedings of International Conference on Intelligent Transportation Systems. Piscataway: IEEE, 1999: 314 – 318. AUTHORS First Author – Abhishek Bhardwaj, M-tech (pursuing), Amity School of engineering and technology, Amity University, Noida, U.P., India; Email-id: abhi14.amity@gmail.com Second Author – Pareesha Aggarwal, M-tech (pursuing), Amity School of engineering and technology, Amity University, Noida, U.P., India; Email-id: paru.aggarwal2112@gmail.com Third Author – Rishi Kumar, Assistant professor, Amity School of engineering and technology, Amity University, Noida, U.P., India; Email-id: rkumar25@amity.edu Correspondence Author – Nidhi Chandra, Assistant professor, Amity School of engineering and technology, Amity University, Noida, U.P., India; Email-id:srivastavanidhi8@gmail.com www.ijsrp.org
International Journal of Scientific and Research Publications, Volume 3, Issue 2, February 2013 706 ISSN 2250-3153 iAssist: An Intelligent Online Assistance System Khanapure V.M 1 , Prof. Chirchi V.R 2 1 P.G Department(CNE), MBES,COE Ambajogai, M.S.India, 2 IEEE Member P.G Department(CNE), MBES,COE Ambajogai, M.S.India, (khanapure.vaishu@gmail.com,vr.chirchi@gmail.com) Abstract- World Wide Web is the most useful source of information. A search engine can support only the initial stages of the search process, i.e., it can locate Web sites where relevant information is available. But, most of the search engines are keyword-based and are not much useful within a Web site to help the user to identify his preferred service. For this purpose, many companies use intelligent assistance systems (e.g., case-based systems) to improve customer service quality. We develop iAssist, an intelligent online assistance system, to automatically find problem solution at terns from the past customer– representative interactions.When a new customer request arrives, iAssist searches and ranks the past cases based on their semantic relevance to the request, groups the relevant cases into different clusters using symmetric matrix factorization, and summarizes each case cluster to generate recommended solutions. Index Terms- Case clustering, case summarization, intelligent assistance, semantic similarity. T I. INTRODUCTION he 70% of the customers hit the road not because of the price or product quality issues but because they do not like the customer service. Current customer service (also called helpdesk, call center, etc.) involves a lot of manual operations, which require customer service representatives to master a large variety of malfunction issues. Moreover, it is difficult to transfer knowledge and experience between representatives. Thus, many companies attempt to build intelligent helpdesk systems to improve the quality of customer service. It is also many online intelligent systems but mainly suffer from keyword matching technologies and error-level information at the solution time. So we have proposed a new algorithm called semantic role parser, similarity score calculation. The main objectives of this automatically find the problem solution. Given a new customer request, one common scenario of an intelligent helpdesk system is to find whether similar requests have been processed before. Helpdesk systems usually use databases to store past interactions (e.g., descriptions of a problem and recommended solutions) between customers and companies. In a case-based system case collects all the information provided by the user during a recommendation session such as the user’s queries to the product catalogues, the selected products, and, in case the user is registered, some stable userrelated preferences and demographic data. However, these casebased systems face the following two challenges. A. Case retrieval measures Given a new request from a customer, most case-based systems search and rank the documents of past cases based on their relevance to the request. Many methods have been proposed to determine the relevance of past cases to requests in database, and to perform similarity search. However, these methods usually use traditional keyword-matching-based ranking schemes, which have difficulty in capturing the semantic meanings of the requests and the past cases. For example, given a request ―can you switch the computers? most case-based systems would return past cases related to network switches. In addition, when the description of the cases or items becomes complicated, these case-based systems also suffer from the curse of dimensionality, and the similarity/distance between cases or items becomes difficult to measure. New similarity measurements that are able to understand the semantic meanings in the requests and the past cases are thus needed. B. Result representation Most case-based systems return a list of past cases ranked by their relevance to a new request. Customers have to go through the list and examine the cases one by one to identify their desired cases. This is a time-consuming task if the list is long. A possible solution is to organize the past cases into different groups, each of which corresponds to a specific context or scenario. This would enable the customers to identify their desired contexts at a glance. It is also necessary to generate a short and concise summary for each context to improve the usability. II. LITERATURE SURVEY A. Case-based systems Some case-based systems is based on keyword matching, which lacks the semantic analysis of customer requests and existing cases[4][6].Thus new similarity measurement are needed that are able to understand the semantic meaning in the request & past cases[1][5]. B. Database search and ranking Similarity is measure based on Keyword matching, which have difficulty to understand text deeply[7]. For finding answers quickly once a new request arrives, cases are rank based on semantic importance[2]. In database search, many methods have been proposed to perform similarity search and rank results of a query. However, similar to the case based systems, the similarity is measured based on keyword matching, which have difficulty to understand the text deeply. www.ijsrp.org
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